Mammogram retrieval on similar mass lesions

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Title:	Mammogram retrieval on similar mass lesions
Authors:	陳攸華;Wei, Chia-Hung;Chen, Sherry Y.;Liu, Xiaohui
Contributors:	資訊電機學院網路學習科技研究所
Keywords:	Algorithms;Biological and medical sciences;Breast cancer;Breast Neoplasms - diagnostic imaging;Content-based image retrieval;Female;Humans;Image Processing, Computer-Assisted - methods;Information Storage and Retrieval - methods;Internal Medicine;Mammography;Medical sciences;Other;Pattern Recognition, Automated - methods;Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects);Technology. Biomaterials. Equipments. Material. Instrumentation
Date:	2012-06-01
Issue Date:	2026-04-23 14:14:58 (UTC+8)
Publisher:	Elsevier Ireland Ltd;Kidlington: Elsevier Ireland Ltd
Abstract:	摘要： Enormous numbers of digital mammograms have been produced in hospitals and breast screening centers. To exploit those valuable resources in aiding diagnoses and research, content-based mammogram retrieval systems are required to effectively access the mammogram databases. This paper presents a content-based mammogram retrieval system, which allows medical professionals to seek mass lesions that are pathologically similar to a given example. In this retrieval system, shape and margin features of mass lesions are extracted to represent the characteristics of mammographic lesions. To compare the similarity between the query example and any lesion within the databases, this study proposes a similarity measure scheme which involves the hierarchical arrangement of mammographic features and a weighting distance measure. This makes similarity measure of the retrieval system consistent with the way radiologists observe mass lesions. This study used the DDSM dataset to evaluate the effectiveness of the extracted shape feature and margin feature, respectively. Experimental results demonstrate that, when Zernike moments are used, round-shape masses are the most discriminative among four types of shape; the circumscribed-margin masses can be effectively discriminated among the four types of margins. Moreover, the result also shows that, when retrieving round-shape and circumscribed margin masses, this retrieval system can achieve the highest precision among all mass lesion types. 其他題名： Comput Methods Programs Biomed 出版者： Kidlington: Elsevier Ireland Ltd 出版日期： 2012-06-01 出處： Computer methods and programs in biomedicine, 2012-06, Vol.106 (3), p.234-248 版權： 2010 Elsevier Ireland Ltd 版權： Elsevier Ireland Ltd 版權： 2015 INIST-CNRS 版權： Copyright © 2010 Elsevier Ireland Ltd. All rights reserved. 識別號： ISSN: 0169-2607 識別號： ISSN: 1872-7565 識別號： EISSN: 1872-7565 識別號： DOI: 10.1016/j.cmpb.2010.09.002 識別號： PMID: 20933295
Appears in Collections:	[Graduate Institute of Network Learning Technology] journal & Dissertation

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